Modern biomedical research is inherently multi-leveled and multi-disciplinary. To facilitate this research, core facilities bring the latest imaging and scanning technologies to the research community and support many projects simultaneously. However, they often do so in the midst of significant information management challenges unforeseen at their inception, such as: (a) effective and efficient distribution of acquired scientific data from a core facility to its investigators; (b) timely sharing of raw, primary, and curated data for collaborative activities; (c) optimized scheduling and resource usage; (d) management of experimental workflow, e.g., multiple related steps in one-time or longitudinal studies; (e) management of administrative workflow, such as tracking of material cost, staff times spent on sample preparation and data acquisition, and billing and accounting; (f) monitoring of the overall resource usage of a core facility, by compiling, e.g., a profile of usage statistics of equipment and types of involved projects; and (g) coherent and common access point for data analysis workflow, linking raw data and/or primary data with results from analyses, reports, images, and references, and comparing with related results from existing databases and literature.
There are currently no comprehensive software systems addressing these challenges as a whole (Siemens' MIPortal focuses on improving the management of experimental workflow for proteomics research and does not address administrative issues). Deficiencies with the existing infrastructure are often manifested in: (i) substantial administrative and personnel overhead. This exists in pen-and-paper-based record keeping aided by disconnected spreadsheet programs, manual management of scheduling on a common off-the-shelf calendar system that operates in isolation, using portable media for data transport, and relying on e-mail communication to gather a variety of project related information. Some centers operate under an information technology (IT) infrastructure resulting from adopting/adapting existing open-source/in house/commercial software for managing a variety of data, although this only reduces the problem to the equally, if not more, challenging issues of information integration, interoperability, and resource for IT personnel support; (ii) lack of support for collaboration among researchers. The disintegration of administrative and scientific data makes it difficult to access data and find information about related prior studies. Collaborating researchers must then rely on ad hoc mechanisms such as email communication to share data and results. This not only makes the bookkeeping of data a chore, but it also lacks a uniformly enforceable standard for the safety of valuable data and results from analyses; (iii) significant amount of redundant, disintegrated, and inconsistent data. When data are kept in disconnected systems, information such as a principal investigator's profile and projects may have to be reentered multiple times to multiple systems, making it difficult to maintain and update. Repetition in data entry not only requires additional effort, but it also opens more room for errors and inconsistencies: the same entities may have been entered using different names in different systems, and changes made in one system may not automatically propagate to other systems; and (iv) lack of support for the integration of information from disparate resources. Access to data and knowledge is often labor-intensive, repetitive, disorganized, and burdensome; project management and data analyses are tasks relegated to individual investigators without a common framework or standard for record keeping or for sharing and collaboration using intermediate results.
The root cause for these deficiencies can be summarized as a lack of a holistic approach to infrastructure support. Given the challenges encountered by imaging and other kinds of core facilities, an approach that captures a vision for a long-term solution and addresses some of the immediate needs is desirable. The present multi-modality multi-resource information integration environment (“MIMI”) not only addresses some of the needs and provides a flexible and expandable solution to the challenges mentioned above, but also provides a foundation for a more advanced system that substantially integrates existing knowledge with analyses and curation of experimental data.
The query interface is increasingly recognized as a bottleneck for the rate of return for investments and innovations in clinical research. Improving query interfaces to clinical databases can only result from an approach that centers around the work requirements and cognitive characteristics of the end-user, not the structure of the data. To date, few interfaces are usable directly by clinical investigators, with the i2b2 web client a possible exception. Aspects of query interface design that facilitate its use by investigators include query-by-example, tree-based construction, being database structure agnostic, obtaining counts in real time before the query is finished and executed, and saving queries for reuse.
Unlike previous art Phyiso-MIMI develops informatics tools to be used directly by researchers to facilitate data access in a federated model for the purposes of hypothesis testing, cohort identification, data mining, and clinical research training. In order to accomplish this goal a new approach to the query interface was necessary.